درخت حوزه‌های تخصصی

Digital Transformation has not been foreign to people in emerging economic growth, especially SME entrepreneurs in developing countries such as Malaysia. Digital transformation adoption is a way for an enterprise to improve the use of technology in a better way. But as the digital revolution has been evolving, there are still a few entrepreneurs who neglect it. The purpose of the present study is to investigate the factors affecting the adoption which is (competitive pressure, technology advancement, cost minimization, and environmental influence) on digital transformation adoption among SMEs in Malaysia. The relationship also consists of a moderating effect which is environment shock (pandemic covid-19) between the independent variable and dependent variable. The study also provides some knowledge for a deeper understanding of digital transformation and a guide to all SME entrepreneurs and others.

Corona Virus (COVID-19) is a major problem among people, and it causes suffering worldwide. Yet, the traditional prediction models are not yet suitably efficient in catching the fundamental expertise as they cannot visualize the difficulty in the health's representation problem areas. This paper states prediction mechanism that uses a model of deep learning called Long Short-Term Memory (LSTM). We have carried this model out on corona virus dataset that obtained from the records of infections, deaths, and recovery cases across the world. Furthermore, producing a dataset which includes features of geographic regions (temperature and humidity) that have experienced severe virus outbreaks, risk factors, spatio-temporal analysis, and social behavior of people, a predictive model can be developed for areas where the virus is likely to spread. However, the outcomes of this study are justifiable to alert the authorities and the people to take precautions.

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.

The Forex market is known as the strongest and richest financial market in the world that has been operating continuously. This market is formed based on the exchange rates of different countries, as well as the prices of oil and gold. The price of currency pairs in the Forex market as the largest market for financial transactions has always been of great importance. The purpose of this article is to study the Forex market based on chaos theory. Its statistical population includes three currency pairs Euro / Dollar, Pound / Dollar and Dollar / Yen. The period of prices of the surveyed currency pairs is from January 2017 to December 2021 and its time range includes daily prices. The results showed that after fitting the model and performing the BDS test, on the GARCH model residues in different dimensions and ε of all 6 groups, which are IID residues and there is no dependence on them. The results of Lyapunov's maximalism test showed that for all dimensions, and all time intervals (n), the value of Lyapunov's exponent is a positive and small number, indicating that the time series of the triple currency pair follows a chaotic process.

This study aims to investigate the effectiveness of university roles in entrepreneurial intentions among university students. This study is also measuring the functions of the system supports as the control variables towards entrepreneurial intentions. Universities seem to compete in the industry to provide and develop the entrepreneurial skills set to their students. Perhaps they may add more benefit in preparing and equipping their students as entrepreneurs once they completed their studies. The study was conducted in local universities. Three hundred respondents were approached in getting the data compiled. Based on that, only 250 complete and usable data received in returns. An 83% response rate is achieved because the sample was from a list of young entrepreneurs' schemes currently being assessed by the government agencies. Questionnaires used to validate the items from university roles (6 items), entrepreneurial intentions (5 items), and finally support systems (6 items). Likert scales were used to measure all the items. Besides that, there are also seven questions for respondents' profiles. The results indicate that all tested hypothesized were positively significant. Universities should consider finding ways to develop and motivating their students towards entrepreneurial intentions. Further study may continue the model with different sets of control variables for concrete solutions in managing the student's entrepreneurial intentions.